Daniel Güldenring, Khaled Rjoob, Raymond R. Bond, Dewar D. Finlay, Victoria McGilligan, Stephen J. Leslie, Ali S. Rababah, Aleeha Iftikhar, Charles Knoery, Anne McShane, Aaron Peace
- This an exploratory paper that discusses the use of artificial intelligence (AI) in ECG interpretation and opportunities for improving the explainability of the AI (XAI) when reading 12-lead ECGs. To develop AI systems, many principles (human rights, well-being, data agency, effectiveness, transparency, accountability, awareness of misuse and competence) must be considered to ensure that the AI is trustworthy and applicable. The current computerised ECG interpretation algorithms can detect different types of heart diseases. However, there are some challenges and shortcomings that need to be addressed, such as the explainability issue and the interaction between the human and the AI for clinical decision making. These challenges create opportunities to develop a trustworthy XAI for automated ECG interpretation with a high performance and a high confidence level. This study reports a proposed XAI interface design in automatic ECG interpretation based on suggestions from previous studies and based on standard guidelines that wereThis an exploratory paper that discusses the use of artificial intelligence (AI) in ECG interpretation and opportunities for improving the explainability of the AI (XAI) when reading 12-lead ECGs. To develop AI systems, many principles (human rights, well-being, data agency, effectiveness, transparency, accountability, awareness of misuse and competence) must be considered to ensure that the AI is trustworthy and applicable. The current computerised ECG interpretation algorithms can detect different types of heart diseases. However, there are some challenges and shortcomings that need to be addressed, such as the explainability issue and the interaction between the human and the AI for clinical decision making. These challenges create opportunities to develop a trustworthy XAI for automated ECG interpretation with a high performance and a high confidence level. This study reports a proposed XAI interface design in automatic ECG interpretation based on suggestions from previous studies and based on standard guidelines that were developed by the human computer interaction (HCI) community. New XAI interfaces should be developed in the future that facilitate more transparency of the decision logic of the algorithm which may allow users to calibrate their trust and use of the AI system.…
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MetadatenAuthor: | Daniel GüldenringORCiD, Khaled Rjoob, Raymond R. BondORCiD, Dewar D. FinlayORCiD, Victoria McGilligan, Stephen J. Leslie, Ali S. Rababah, Aleeha Iftikhar, Charles Knoery, Anne McShane, Aaron Peace |
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DOI: | https://doi.org/10.1007/978-3-030-68007-7_6 |
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Identifier: | 978-3-030-68006-0 OPAC HS OPAC extern |
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Parent Title (English): | Advanced Visual Interfaces. Supporting Artificial Intelligence and Big Data Applications |
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Subtitle (English): | AVI 2020 Workshops, AVI-BDA and ITAVIS, Ischia, Italy, June 9, 2020 and September 29, 2020, Revised Selected Papers |
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Publisher: | Springer Nature Switzerland |
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Document Type: | conference proceeding (article) |
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Conference: | AVI 2020 Workshops, AVI-BDA and ITAVIS, Ischia, Italy, June 9, 2020 and September 29, 2020 |
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Language: | English |
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Date of Publication (online): | 2021/02/03 |
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Year of first Publication: | 2021 |
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Tag: | Biomedizinische Signalverarbeitung; EKG; ECG |
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Volume: | 2021 |
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Number of pages: | 13 Seiten |
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First Page: | 96 |
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Last Page: | 108 |
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Institutes: | Fakultät Elektrotechnik |
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Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften |
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Research focus: | Eigenständige Forschungsfelder |
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Publication Lists: | Güldenring, Daniel |
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Publication reviewed: | begutachtet |
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Release Date: | 2021/06/16 |
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